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Visualizing Movement Control Optimization Landscapes.

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    This study visualizes complex movement control optimization landscapes, revealing why optimization is challenging. Techniques like early termination and specific parameterizations simplify this process for animation research.

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    Area of Science:

    • Computer Graphics
    • Robotics
    • Animation

    Background:

    • Movement control optimization is crucial in animation and robotics.
    • Limited understanding exists due to the lack of closed-form objective functions.

    Purpose of the Study:

    • To visualize high-dimensional control optimization landscapes.
    • To understand challenges in movement control optimization.
    • To evaluate common optimization practices.

    Main Methods:

    • Novel visualizations of optimization landscapes.
    • Analysis of neural network training.
    • Empirical data collection on trajectory and policy optimization.

    Main Results:

    • Visualizations reveal optimization difficulties.
    • Early termination and spline parameterizations ease optimization.
    • Partial target state parameterization acts as a preconditioner.
    • Neural network policy optimization scales better for long horizons.

    Conclusions:

    • Movement control optimization is complex but can be simplified.
    • Visualizations offer insights into optimization challenges.
    • Neural network policies show promise for long-term planning in animation.